
"""simple docstring"""



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)




@dataclass
class 		a_    (			_UpperCAmelCase						):
    a   :					Union[List[PIL.Image.Image], np.ndarray]
    a   :					Optional[List[bool]]


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .pipeline_cycle_diffusion import CycleDiffusionPipeline
 from .pipeline_stable_diffusion import StableDiffusionPipeline
 from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
 from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
 from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
 from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
 from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
 from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
 from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
 from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
 from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
 from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
 from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
 from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
 from .pipeline_stable_unclip import StableUnCLIPPipeline
 from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
 from .safety_checker import StableDiffusionSafetyChecker
 from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
 if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
 from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
 if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.26.0''')):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import (
     StableDiffusionDepthaImgPipeline,
     StableDiffusionDiffEditPipeline,
     StableDiffusionPixaPixZeroPipeline,
 )
else:
 from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
 from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
 from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
 if not (
     is_torch_available()
     and is_transformers_available()
     and is_k_diffusion_available()
     and is_k_diffusion_version('''>=''', '''0.0.12''')
 ):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
 from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
 if not (is_transformers_available() and is_onnx_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_onnx_objects import *  # noqa F403
else:
 from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
 from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
 from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
 from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
 from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
 import flax




 @flax.struct.dataclass
 class 		a_    (			_UpperCAmelCase						):
     a   :					np.ndarray
     a   :					List[bool]







 from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
 from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
 from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
 from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
 from .safety_checker_flax import FlaxStableDiffusionSafetyChecker

"""simple docstring"""



import os
from math import logaa


def 			_UpperCamelCase						(    _A = "base_exp.txt"   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = 0
    _UpperCAmelCase        = 0
    for i, line in enumerate(open(os.path.join(os.path.dirname(_A   )       ,   _A   )   )   ):
        _UpperCAmelCase     ,_UpperCAmelCase        = list(map(_A       ,   line.split(""","""   )   )   )
        if x * logaa(_A   ) > largest:
            _UpperCAmelCase        = x * logaa(_A   )
            _UpperCAmelCase        = i + 1
    return result


if __name__ == "__main__":
 print(solution())